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Thursday16 items

  6. dongxi_nlpResearch
    AI score62

    English translation is being prepared.

    Quoted postWhite Circle · @whitecircle

    We are open-sourcing RISE, our red-teaming algorithm that breaks the safeguards of the latest image models from OpenAI, DeepMind, and others. RISE uses readily available models like Qwen and Gemini Flash under the hood, with no additional training required. Read more: https://t.co/kt8ub0OmeW

    #Safety / alignment#Image generation#Evaluations / benchmarks

  7. rohan_paulResearch
    AI score60

    English translation is being prepared.

    Quoted postJiaru "Rubin" Zou · @Jiaru_Zou

    Your LLM agent has to think out loud, token by token, before it can search, run code, or act in the world. We humans don’t. We ponder in silence and speak when it matters. Human language was never meant to be the limit of machine thought. Introducing 🌗Hybrid Reasoning Agents - Think in latent space, act through text - Two modes, one unified multi-turn flow - No black box, fully readable latent thoughts - Any LLM can become a HybridAgent

    #Papers/Research#Agents#Reasoning

Wednesday19 items

  1. Ma Dongxi NLPResearch
    AI score52

    AutoEnvScaling: Automating the Data Flywheel with Terminal Agents

    Researchers propose AutoEnvScaling, which turns the data flywheel into a terminal task, where a proposer agent builds complete Harbor tasks and sandboxes just like a human researcher. This approach supports self-play reinforcement learning for terminal agents, with the same model continuously improving as both builder and solver, without humans in the loop.

    Quoted postSimon Yu ✈️ EMNLP2026 · @simon_ycl

    Recursive self-improvement needs agents that can build their own training environments. But researchers still part of the loop, making it hard to scale. We introduce AutoEnvScaling, which turns Data Flywheel into a terminal task: a proposer agent builds complete Harbor tasks and sandbox. It builds just like human researchers. This enables self-play RL for terminal agents: one model improves as both builder and solver, without human-in-the-loop.

    #Papers/Research#Agents#English text is being prepared.